Experimental investigation, numerical modelling and multi-objective optimisation of composite wind turbine blades

Static load and modal testing of two blades from a 15 kW wind turbine is presented. The two blades are made from glass fibre-reinforced polypropylene, one of which has been reinforced with additional carbon fibre plies. Static testing is performed with a Whiffle tree test rig to determine the structural response of the blades. Blade mass, deflections, strains and natural frequencies are reported. The following objectives are undertaken: (i) evaluate and compare the test results of the two wind turbine blade designs, (ii) use the results to validate finite element models of the blades and (iii) utilise the validated models in a design optimisation study. Parametric blade models are generated using the Python programming language and are based on manufacturing specifications for the blades. The models show good correspondence with the experimental results. The goal of the optimisation study is to maximise the stiffness and reduce the mass of the glass fibre blade. A multi-objective genetic algorithm is used to determine the optimum laminate thicknesses along the length of the blades. The optimisation study produced a set of Pareto efficient blade designs with up to 17% improvement in stiffness and 30% reduction in mass for the glass fibre blade design.This material is in part based upon works supported by the Science Foundation Ireland Marine Renewable Energy Ireland (MaREI) research centre under Grant No. 12/RC/2302. It was also funded by a fellowship from the College of Engineering and Informatics, NUI Galway, and was supported by an NUI Travelling Studentship, 2014. The last author would like to acknowledge the support of Science Foundation Ireland through the Career Development Award programme (Grant No. 13/CDA/2200). Additional thanks are given to the technical staff at NUI Galway and the group of mechanical engineering Masters students for their efforts on the testing of the wind turbine blades. Thanks also to Dr Jonathan Byrne for his guidance on the implementation of the NSGA-II algorithm.peer-reviewe